Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning.
This comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity.
Audience
This book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.
Table of Contents
Preface xv
Acknowledgment xvii
1 Performance Evaluation of Avionics System Under Hardware-In- Loop Simulation Framework with Implementation of an AS9100 Quality Management System 1
Rajesh Shankar Karvande and Tatineni Madhavi
1.1 Introduction 2
1.2 HILS Process and Quality Management System 4
1.3 HILS Testing Phase 7
1.4 AS9100 QMS Integrated with HILS Process 8
1.5 Conclusion and Suggestions 10
References 10
2 YouTube Comment Summarizer and Time-Based Analysis 13
Preeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar
2.1 Introduction 13
2.2 Literature Review 16
2.3 Methodology 18
2.3.1 YouTube Comments Data Collection 18
2.3.1.1 YouTube Data API Integration 18
2.3.1.2 get_video_comments Function 19
2.3.1.3 Comment Processing 19
2.3.1.4 Handling Pagination with get_all_video_ comments 20
2.3.1.5 Excel File Creation with save_to_excel 20
2.3.2 Datasets 20
2.3.3 Extractive Summarization 21
2.4 Result 30
2.5 Performance 30
2.6 Conclusion 31
References 31
3 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions 33
Premanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar
3.1 Introduction 34
3.2 Related Works 34
3.3 Methodology 36
3.4 Comparision with Existing Systems 41
3.5 Future Scope 48
3.6 Conclusion 48
Acknowledgment 49
References 49
4 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction 51
Ramya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani
4.1 Introduction 52
4.2 Related Works 53
4.3 Proposed Framework 56
4.3.1 ML Models and Ablation Study 56
4.3.2 Building Ensemble Model Using AdaBoost 57
4.4 Experimental Setup 58
4.4.1 Dataset 58
4.4.2 Data Visualization 59
4.4.3 Data Pre-Processing Phase 60
4.4.4 Proposed Methodology 61
4.4.5 Performance Metrics 62
4.5 Results and Discussion 63
4.5.1 Comparison with Baseline Models 63
4.5.2 Comparison with Existing Literature Works 66
4.6 Existing Works 67
4.7 Conclusion and Future Work 69
Dataset 69
References 69
5 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics 73
Shelly Prakash and Vaibhav Vyas
5.1 Introduction 74
5.1.1 Cloud Computing 75
5.1.1.1 Fault, Error, Failure 75
5.2 Related Work 77
5.3 Proposed Proactive Fault Detection Architecture 81
5.4 Conclusion 95
References 95
6 FlowGuard: Efficient Traffic Monitoring System 99
Varsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari
6.1 Introduction 99
6.2 Literature Review 100
6.3 Methodology 113
6.3.1 Theory 113
6.3.2 Requirement 114
6.3.2.1 Hardware Requirements 114
6.3.2.2 Software Requirements 116
6.3.3 Workflow 117
6.3.4 Flowchart 118
6.4 Results and Discussions 118
6.5 Conclusion 121
6.6 Future Scope 121
Acknowledgment 122
References 122
References for Pictures of Components Used 124
7 A Survey on Heart Disease Prediction Using Ensemble Techniques in ml 125
Sudhakar Vecha and M.V.P. Chandra Sekhara Rao
7.1 Introduction 125
7.2 Literature Survey 127
7.3 Datasets 128
7.4 Ensemble Learning in Heart Disease 129
7.5 Challenges and Limitations 134
7.6 Future Directions 134
7.7 Conclusion 135
References 135
8 A Video Surveillance: Crowd Anomaly Detection and Management Alert System 139
Anitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath
8.1 Introduction 140
8.2 Related Work 140
8.3 Dataset Description 143
8.4 Problem Definition 143
8.5 Proposed Methodology and System 144
8.5.1 Proposed Methodology 144
8.5.2 Proposed System 146
8.6 Results 148
8.7 Conclusion and Future Scope 150
8.7.1 Conclusion 150
8.7.2 Future Scope 151
References 151
9 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances 153
Shatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule
9.1 Introduction 154
9.1.1 Parallelism 154
9.1.2 Quantum Speedup 155
9.1.3 Quantum Entanglement 155
9.1.4 Quantum Fourier Transform 155
9.1.5 Quantum Machine Learning Algorithms 155
9.1.6 Quantum Data Representation 155
9.1.7 Quantum Sampling 155
9.1.8 Quantum Annealing 156
9.1.9 Hybrid Quantum-Classical Approaches 156
9.2 Review of Literature 156
9.2.1 Overview of Key Quantum Computing Principles 156
9.2.1.1 Qubits (Quantum Bits) 157
9.2.1.2 Quantum Gates 157
9.2.1.3 Quantum Parallelism 157
9.2.1.4 Quantum Measurement 157
9.2.1.5 Quantum Fourier Transform 158
9.2.1.6 Quantum Entanglement-Based Algorithms 158
9.3 Basic Quantum Operations, Qubits, and Quantum Gates 158
9.3.1 Basic Quantum Operations 158
9.3.2 Quantum Bits (Qubits) 158
9.3.3 Quantum Gates 159
9.4 Quantum Machine Learning Algorithms 159
9.4.1 Quantum Support Vector Machines (QSVM) 161
9.4.2 Quantum Neural Networks (QNN) 161
9.4.3 Quantum Clustering Algorithms 161
9.4.4 Quantum Principal Component Analysis (QPCA) 162
9.4.5 Quantum Boltzmann Machines 162
9.4.6 Quantum Support Vector Clustering (QSVC) 162
9.5 Quantum Hardware for Machine Learning 162
9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware 163
9.6.1 Decoherence and Quantum Error Correction 163
9.6.2 Quantum Gate Fidelity 163
9.6.3 Scalability 164
9.6.4 Qubit Connectivity and Crosstalk 164
9.6.5 Material Science and Qubit Implementation 164
9.6.6 Quantum Interconnects 164
9.6.7 Thermal Management 164
9.6.8 Error Mitigation Strategies 164
9.7 Challenges and Limitations in Quantum Machine Learning 165
9.7.1 Quantum Computational Overheads 165
9.7.2 Hybrid Quantum-Classical System Integration 165
9.7.3 Limited Quantum Expressibility 165
9.7.4 Data Preprocessing Challenges 165
9.7.5 Quantum Algorithm Verification 166
9.7.6 Quantum Resource Requirements 166
9.7.7 Adaptation to Quantum Hardware Constraints 166
9.7.8 Limited Quantum Hardware Availability 166
9.7.9 Algorithmic Complexity 166
9.7.10 Quantum Model Interpretability 166
9.8 Future Directions 167
9.9 Conclusion 167
References 167
10 Multi-Band Self-Grounding Antenna for Wireless Technologies 169
Ch. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu
10.1 Introduction 170
10.1.1 Literature Review 170
10.2 Design of Antenna 174
10.2.1 Design and Results at Primary Level of Antenna 175
10.2.2 Design and Results at Secondary Level of Antenna 175
10.3 Actual Design of Antenna 176
10.4 Results of Antenna 176
10.4.1 Mathematical Analysis 178
10.4.2 3D Polar Plot 178
10.5 Conclusions 179
References 180
11 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies 183
Sai Ramya, Smera C. and Sandeep J.
11.1 Introduction 184
11.2 Related Work 186
11.3 Methodology 194
11.4 Conclusion 197
References 197
12 File Fragment Classification: A Comprehensive Survey of Research Advances 201
Teena Mary and Sreeja C.S.
12.1 Introduction 201
12.2 Methodology 203
12.2.1 Selection Criteria 203
12.2.2 Structure of the Paper 204
12.3 Approaches for File Fragment Classification 204
12.3.1 Signature-Based Approaches 204
12.3.2 Content-Based Approaches 206
12.3.3 Deep Learning-Based Approaches 207
12.3.3.1 Convolutional Neural Networks (CNNs) 208
12.3.3.2 Feed Forward Neural Networks (FFNNs) 209
12.3.4 Hierarchical Classification Methods 209
12.4 Survey Findings 210
12.5 Challenges and Future Directions 214
12.6 Conclusion 215
References 216
13 Deepfake Detection and Forensic Precision for Online Harassment 219
K. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha
13.1 Introduction 220
13.2 Literature 221
13.3 Theoretical Analysis and Software Simulation 222
13.3.1 Theoretical Analysis 222
13.3.2 Software Simulation 223
13.3.3 Testing and Optimization 224
References 225
14 Design of Automatic Seed Sowing Machine 227
Chiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar
14.1 Introduction 228
14.2 Literature Survey 229
14.3 Proposed System 232
14.4 Conclusions 235
References 235
15 In Motion: Exploring Urban Rides Through Data Analytics 237
Rajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan
15.1 Introduction 237
15.2 Literature Survey 238
15.3 Proposed Methodology 240
15.4 Result Analysis 247
15.5 Conclusion 248
References 249
16 Design of Novel Chatbot Using Generative Artificial Intelligence 251
Sk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma
16.1 Introduction 252
16.2 Conclusion and Future Scope 257
References 257
17 The Smart Nebulizer Cap for Enhanced Asthma Management 259
Rossly Netala, Aadi Praharsha and Mohan Dholvan
17.1 Introduction 259
17.2 Literature Survey 261
17.3 Methodology 262
17.4 Conclusions 265
References 265
18 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 267
Y. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi
18.1 Introduction 268
18.2 Literature Survey 269
18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 271
18.4 Result Analysis 274
18.5 Conclusion 276
References 277
19 Intrusion Detection System Using Machine Learning 279
Ballikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan
19.1 Introduction 280
19.2 Literature Survey 280
19.3 Methodology 281
19.4 Algorithm 283
19.5 Implementation 285
19.6 Results and Outputs 289
19.6.1 User Interface 289
19.7 Conclusion and Future Scope 290
References 291
20 Prediction of Arrival Delay Time in Freightage Rails 293
Bobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M.
20.1 Introduction 294
20.2 Literature Survey 295
20.3 Methodology 297
20.4 Experimental Results 302
20.5 Conclusions 308
References 309
21 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers 311
L. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan
21.1 Introduction 311
21.2 Literature Survey 312
21.3 Proposed Methodology 314
21.4 Result Analysis 322
21.5 Conclusion 322
References 323
22 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study 325
Venkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu
22.1 Introduction 326
22.2 Implementation of RCA & CSA 328
22.3 Simulation Results 333
22.4 Conclusion 335
References 335
23 XGBoost Classifier Based Water Quality Classification Using Machine Learning 337
Nagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan
23.1 Introduction 338
23.2 Related Work 338
23.3 Proposed Methodology 339
23.4 Results and Discussion 342
23.5 Conclusion 345
References 345
Index 347
